code: def test_choose_model():
      df = pd.read_csv(
          "tests/unit/ml_handlers/data/anomaly_detection.csv"
      )
      # If no target is specified, we should use the unsupervised model
      model = choose_model(df)
      assert model.__class__.__name__ == "ECOD"
      # If the size of the dataset is less than the semi_supervised_threshold, we should use the semi-supervised model
      model = choose_model(df, target="class", supervised_threshold=50)
      assert model.__class__.__name__ == "XGBOD"
      # If the model type is specified, we should use that model type and override default logic
      model = choose_model(
          df,
          target="class",
          model_type="supervised",
          supervised_threshold=50,
      )
      assert model.__class__.__name__ == "CatBoostClassifier"
      # If the size of the dataset is greater than the semi_supervised_threshold, we should use the supervised model
      model = choose_model(df, target="class", supervised_threshold=2)
      assert model.__class__.__name__ == "CatBoostClassifier"
      # If the model type is specified, we should use that model type and override default logic
      model = choose_model(
          df,
          target="class",
          model_type="semi-supervised",
          supervised_threshold=2,
      )
      assert model.__class__.__name__ == "XGBOD"
      # If the model name is specified, we should use that model name and override default logic
      model = choose_model(
          df, target=None, model_name="knn", supervised_threshold=2
      )
      assert model.__class__.__name__ == "KNN"
      # If the model does not exist, we should raise an error
      try:
          model = choose_model(
              df,
              target="class",
              model_name="not_a_model",
              supervised_threshold=2,
          )
          assert False
      except AssertionError:
          assert True
  
file_path: mindsdb/tests/unit/ml_handlers/test_anomaly_detection.py
imports: import time
  import pandas
  from unittest.mock import patch
  from mindsdb_sql import parse_sql
  from tests.unit.executor_test_base import BaseExecutorTest
  from mindsdb.integrations.handlers.anomaly_detection_handler.anomaly_detection_handler import choose_model, preprocess_data
label_desc: Use image node_id 1 for calling a global function with example usage: test_choose_model() without return types